Towards the maintenance of population diversity: A hybrid genetic network programming
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- @Article{Li:2010:TJSEC,
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author = "Xianneng Li and Shingo Mabu and Kotaro Hirasawa",
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title = "Towards the maintenance of population diversity: A
hybrid genetic network programming",
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journal = "Transaction of the Japanese Society for Evolutionary
Computation",
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year = "2010",
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volume = "1",
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number = "1",
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pages = "89--101",
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month = "12",
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email = "sennou@asagi.waseda.jp",
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keywords = "genetic algorithms, genetic programming, genetic
network programming, probabilistic model building
evolutionary algorithm, PMBEA, estimation of
distribution algorithm, EDA, GNP, probabilistic model
building genetic network programming, PMBGNP, diversity
maintenance",
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ISSN = "2185-7385",
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URL = "http://www.jpnsec.org/online_journal/1_1/1_89.pdf",
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size = "13 pages",
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abstract = "Some researchers have investigated that the diversity
loss will significantly decrease the performance of
Probabilistic Model Building Genetic Algorithm (PMBGA),
especially under large search space, leading to the
premature convergence and local optimum. However, few
work has been done on the diversity maintenance in the
Probabilistic Model Building Evolutionary Algorithms
(PMBEAs) with more complex chromosome structures, such
as tree structure based Probabilistic Model Building
Genetic Programming (PMBGP) and graph structure based
Probabilistic Model Building Genetic Network
Programming (PMBGNP). For the PMBEAs with more complex
chromosome structures, the required sample size is
usually much larger than that of binary structure based
PMBGA. Therefore, these algorithms usually become much
more sensitive to the population diversity. In order to
obtain enough population diversity, the large
population size is needed, which is not the best way.
the maintenance of the population diversity is studied
in PMBGNP, which is a kind of PMBEA, but has its unique
characteristics because of its directed graph
structure. This paper proposed a hybrid PMBGNP
algorithm to maintain the population diversity to avoid
the premature convergence and local optimum, and
presented a theoretical analysis of the diversity loss
in PMBGA, PMBGP and PMBGNP. Two techniques have been
proposed for the diversity maintenance when the
population size is set at not large values, which are
multiple probability vectors and genetic operators. The
proposed algorithm is applied and evaluated in a kind
of autonomous robot, Khepera robot. The simulation
study demonstrates that the proposed hybrid PMBGNP is
often able to achieve a better performance than the
conventional algorithms.",
- }
Genetic Programming entries for
Xianneng Li
Shingo Mabu
Kotaro Hirasawa
Citations